mlsanityData to 7 Oct 2026

FLEX: FLEXible Federated Learning Framework

arXiv 2404.06127 ↗First posted 9 Apr 2024cs.CR cs.AIcited by 4

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Abstract (arXiv)

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

20 of 30 references.

References · 20

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8 more
  1. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distancecited by 2
  2. 2025Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selectioncited by 0
  3. 2026Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learningcited by 0
  4. 2026Federated Rule Ensemble Method in Medical Datacited by 0